{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f45741ac-0aa1-44c0-917f-b0d3ee2990fd",
   "metadata": {},
   "source": [
    "# Grain Size Analysis Template\n",
    "\n",
    "> **INFO**  \n",
    "> This is the template for a general grain size analysis. The specific documentation can be found at the following link:  \n",
    "> https://github.com/marcoalopez/GrainSizeTools/wiki/2.-Quantifying-grain-size-populations-using-GrainSizeTools-Script  \n",
    ">\n",
    "> The template shows typical examples of grain size population analysis and different strategies for presenting them. Modify, delete and add as necessary to create your own analysis procedure.\n",
    ">\n",
    "> If you find any error in this template, please report them at https://github.com/marcoalopez/GrainSizeTools/issues  \n",
    "> If you have any questions or suggestions open a discussion at https://github.com/marcoalopez/GrainSizeTools/discussions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "10df5230",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6d4b773-f3cd-4cb5-8b63-027062c090c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "======================================================================================\n",
      "Welcome to GrainSizeTools script\n",
      "======================================================================================\n",
      "A free open-source cross-platform script to visualize and characterize grain size\n",
      "population and estimate differential stress via paleopizometers.\n",
      "\n",
      "Version: 3.2.0\n",
      "Documentation: https://github.com/marcoalopez/GrainSizeTools/wiki\n",
      "\n",
      "Type function_list() to get a list of the main methods\n",
      "\n",
      "To access the piezometry module and the piezometer database, enter the following\n",
      "command: %run piezometers.py\n",
      "\n",
      "module plot imported\n",
      "module averages imported\n",
      "module stereology imported\n",
      "module template imported\n"
     ]
    }
   ],
   "source": [
    "# Load the script. It is assumed that the notebook is in the same folder\n",
    "# as the GrainSizeTools.py file, if not specify the full path to the file.\n",
    "# e.g. %run filepath...\\GrainSizeTools_script.py\n",
    "%run GrainSizeTools_script.py"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53732365",
   "metadata": {},
   "source": [
    "## Data reading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3ff1a5ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# specify your file(s) to be analysed here\n",
    "dataset = pd.read_csv('DATA\\data_set.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c23c16e",
   "metadata": {},
   "source": [
    "The above example loads the data into a variable named ``dataset``, assumes that the data is stored in a file named ``data_set.txt`` located within the ``DATA`` folder, and that the column separator (or delimiter) is a tab space denoted as ``\\t``. For more details see documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1473a67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Area</th>\n",
       "      <th>Circ</th>\n",
       "      <th>Feret</th>\n",
       "      <th>FeretX</th>\n",
       "      <th>FeretY</th>\n",
       "      <th>FeretAngle</th>\n",
       "      <th>MinFeret</th>\n",
       "      <th>AR</th>\n",
       "      <th>Round</th>\n",
       "      <th>Solidity</th>\n",
       "      <th>ECD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>157.25</td>\n",
       "      <td>0.680</td>\n",
       "      <td>18.062</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>131.634</td>\n",
       "      <td>13.500</td>\n",
       "      <td>1.101</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.937</td>\n",
       "      <td>14.149803</td>\n",
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       "      <td>2059.75</td>\n",
       "      <td>0.771</td>\n",
       "      <td>62.097</td>\n",
       "      <td>753.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>165.069</td>\n",
       "      <td>46.697</td>\n",
       "      <td>1.314</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.972</td>\n",
       "      <td>51.210889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1961.50</td>\n",
       "      <td>0.842</td>\n",
       "      <td>57.871</td>\n",
       "      <td>727.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>71.878</td>\n",
       "      <td>46.923</td>\n",
       "      <td>1.139</td>\n",
       "      <td>0.878</td>\n",
       "      <td>0.972</td>\n",
       "      <td>49.974587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5428.50</td>\n",
       "      <td>0.709</td>\n",
       "      <td>114.657</td>\n",
       "      <td>1494.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>19.620</td>\n",
       "      <td>63.449</td>\n",
       "      <td>1.896</td>\n",
       "      <td>0.528</td>\n",
       "      <td>0.947</td>\n",
       "      <td>83.137121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>374.00</td>\n",
       "      <td>0.699</td>\n",
       "      <td>29.262</td>\n",
       "      <td>2328.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>33.147</td>\n",
       "      <td>16.000</td>\n",
       "      <td>1.515</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.970</td>\n",
       "      <td>21.821815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2656</th>\n",
       "      <td>452.50</td>\n",
       "      <td>0.789</td>\n",
       "      <td>28.504</td>\n",
       "      <td>1368.0</td>\n",
       "      <td>1565.5</td>\n",
       "      <td>127.875</td>\n",
       "      <td>22.500</td>\n",
       "      <td>1.235</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.960</td>\n",
       "      <td>24.002935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2657</th>\n",
       "      <td>1081.25</td>\n",
       "      <td>0.756</td>\n",
       "      <td>47.909</td>\n",
       "      <td>1349.5</td>\n",
       "      <td>1569.5</td>\n",
       "      <td>108.246</td>\n",
       "      <td>31.363</td>\n",
       "      <td>1.446</td>\n",
       "      <td>0.692</td>\n",
       "      <td>0.960</td>\n",
       "      <td>37.103777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2658</th>\n",
       "      <td>513.50</td>\n",
       "      <td>0.720</td>\n",
       "      <td>32.962</td>\n",
       "      <td>1373.0</td>\n",
       "      <td>1586.0</td>\n",
       "      <td>112.286</td>\n",
       "      <td>20.496</td>\n",
       "      <td>1.493</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.953</td>\n",
       "      <td>25.569679</td>\n",
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       "    <tr>\n",
       "      <th>2659</th>\n",
       "      <td>277.75</td>\n",
       "      <td>0.627</td>\n",
       "      <td>29.436</td>\n",
       "      <td>1316.0</td>\n",
       "      <td>1601.5</td>\n",
       "      <td>159.102</td>\n",
       "      <td>17.002</td>\n",
       "      <td>1.727</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.920</td>\n",
       "      <td>18.805379</td>\n",
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       "      <td>0.748</td>\n",
       "      <td>39.437</td>\n",
       "      <td>1335.5</td>\n",
       "      <td>1615.5</td>\n",
       "      <td>129.341</td>\n",
       "      <td>28.025</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.740</td>\n",
       "      <td>0.960</td>\n",
       "      <td>30.382539</td>\n",
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       "<p>2661 rows × 11 columns</p>\n",
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      ],
      "text/plain": [
       "         Area   Circ    Feret  FeretX  FeretY  FeretAngle  MinFeret     AR  \\\n",
       "0      157.25  0.680   18.062  1535.0     0.5     131.634    13.500  1.101   \n",
       "1     2059.75  0.771   62.097   753.5    16.5     165.069    46.697  1.314   \n",
       "2     1961.50  0.842   57.871   727.0    65.0      71.878    46.923  1.139   \n",
       "3     5428.50  0.709  114.657  1494.5    83.5      19.620    63.449  1.896   \n",
       "4      374.00  0.699   29.262  2328.0    34.0      33.147    16.000  1.515   \n",
       "...       ...    ...      ...     ...     ...         ...       ...    ...   \n",
       "2656   452.50  0.789   28.504  1368.0  1565.5     127.875    22.500  1.235   \n",
       "2657  1081.25  0.756   47.909  1349.5  1569.5     108.246    31.363  1.446   \n",
       "2658   513.50  0.720   32.962  1373.0  1586.0     112.286    20.496  1.493   \n",
       "2659   277.75  0.627   29.436  1316.0  1601.5     159.102    17.002  1.727   \n",
       "2660   725.00  0.748   39.437  1335.5  1615.5     129.341    28.025  1.351   \n",
       "\n",
       "      Round  Solidity        ECD  \n",
       "0     0.908     0.937  14.149803  \n",
       "1     0.761     0.972  51.210889  \n",
       "2     0.878     0.972  49.974587  \n",
       "3     0.528     0.947  83.137121  \n",
       "4     0.660     0.970  21.821815  \n",
       "...     ...       ...        ...  \n",
       "2656  0.810     0.960  24.002935  \n",
       "2657  0.692     0.960  37.103777  \n",
       "2658  0.670     0.953  25.569679  \n",
       "2659  0.579     0.920  18.805379  \n",
       "2660  0.740     0.960  30.382539  \n",
       "\n",
       "[2661 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# estimate Equivalent Circular Diameters based on areas (if neccesary)\n",
    "dataset['ECD'] = 2 * np.sqrt(dataset['Area'] / np.pi)\n",
    "\n",
    "# display a view of the dataset \n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a705cd",
   "metadata": {},
   "source": [
    "Note the new ``ECD`` column.\n",
    "\n",
    "## Grain size statistics\n",
    "\n",
    "The ``summarize()`` method is responsible for describing the population of grain sizes. By default, this method returns **several common averages** (central tendency estimators) with corresponding confidence intervals at the 95% level (2-sigma) and (2) **several statistical parameters describing the distribution of grain sizes**. The method will automatically select the most optimal methods for estimating confidence intervals for each of the averages (see documentation for details)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8d33308d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "============================================================================\n",
      "CENTRAL TENDENCY ESTIMATORS\n",
      "============================================================================\n",
      "Arithmetic mean = 34.79 microns\n",
      "Confidence intervals at 95.0 %\n",
      "CLT (ASTM) method: 34.09 - 35.48, (±2.0%), length = 1.393\n",
      "============================================================================\n",
      "Geometric mean = 30.10 microns\n",
      "Confidence interval at 95.0 %\n",
      "CLT method: 29.47 - 30.75 (-2.1%, +2.2%), length = 1.283\n",
      "============================================================================\n",
      "Median = 31.53 microns\n",
      "Confidence interval at 95.0 %\n",
      "robust method: 30.84 - 32.81 (-2.2%, +4.1%), length = 1.970\n",
      "============================================================================\n",
      "Mode (KDE-based) = 24.31 microns\n",
      "Maximum precision set to 0.1\n",
      "KDE bandwidth = 4.01 (silverman rule)\n",
      " \n",
      "============================================================================\n",
      "DISTRIBUTION FEATURES\n",
      "============================================================================\n",
      "Sample size (n) = 2661\n",
      "Standard deviation = 18.32 (1-sigma)\n",
      "Interquartile range (IQR) = 23.98\n",
      "Lognormal shape (Multiplicative Standard Deviation) = 1.75\n",
      "============================================================================\n",
      "Shapiro-Wilk test warnings:\n",
      "Data is not normally distributed!\n",
      "Normality test: 0.95, 0.00 (test statistic, p-value)\n",
      "Data is not lognormally distributed!\n",
      "Lognormality test: 0.98, 0.00 (test statistic, p-value)\n",
      "============================================================================\n"
     ]
    }
   ],
   "source": [
    "# Describe grain size population\n",
    "summarize(dataset['ECD'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd110fb",
   "metadata": {},
   "source": [
    "You can modify the behaviour of this function using various parameters. For more details, go to the documentation at https://github.com/marcoalopez/GrainSizeTools/wiki/2.-Quantifying-grain-size-populations-using-GrainSizeTools-Script   or type ``summarize()?`` in a cell and run it.\n",
    "\n",
    "## Plotting grain size populations\n",
    "\n",
    "All the statistical parameters calculated above only make sense if your population is unimodal. It is therefore imperative to **always display the grain size distribution**. The default plot for this is to display the distribution on a linear scale. The ``plot.distribution()`` function takes care of this. By default it shows the distribution using the histogram and the Kernel Density Estimator or KDE (i.e. the continuous line), as well as the location of the various averages. This function also allows you to change some default values, including the type of plot (histogram, kde) or the type of average(s) to be displayed. You can also adjust the binsize of the histogram or the kernel of the KDE, use the ``?`` help command or the script documentation for specific details. You can also modify the parameters of the plot itself, such as the axis labels, the font size, etc. Some examples are commented on below and there are also more examples in the documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dce1c63e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "Histogram features:\n",
      "Number of classes =  45\n",
      "binsize =  3.41\n",
      "=======================================\n",
      "=======================================\n",
      "Kernel density estimate (KDE) features:\n",
      "Bandwidth =  4.01\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax = plot.distribution(dataset['ECD'])\n",
    "\n",
    "# uncomment the following lines (remove the # at the beginning) or add new oones to change the figure defaults\n",
    "#ax.set_xlabel('diameters $\\mu m$', fontsize=14)  # modify x label\n",
    "#ax.set_ylabel('probability', fontsize=14)       # modify y label\n",
    "#ax.set_xticks([0, 50, 100, 150])                # set custom xticks, use "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ca24dd93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment the line below to save the figure\n",
    "#fig1.savefig('distribution_default_plot.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "30789513",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.03, 1.03)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# create the plot\n",
    "fig2, ax = plt.subplots(constrained_layout=True)\n",
    "\n",
    "ax.ecdf(dataset['ECD'])\n",
    "\n",
    "ax.set_xlabel('apparent diameter $\\mu m$')\n",
    "ax.set_ylabel(\"Probability of occurrence\")\n",
    "ax.set_ylim(bottom=-0.03, top=1.03)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6ae3ba76",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment the line below to save the figure\n",
    "#fig2.savefig('distribution_ecdf_plot.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c261e3b",
   "metadata": {},
   "source": [
    "## Testing lognormality"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f30a2b7",
   "metadata": {},
   "source": [
    "Sometimes it can be helpful to test whether the data follow or deviate from a lognormal distribution. For example, to find out if the data are suitable for using the two-step stereological method, or to choose which confidence interval method is optimal.\n",
    "\n",
    "The script uses two methods to test whether the grain size distribution follows a lognormal distribution. One is a visual method called quantile-quantile or _q-q_ plot and the other is a quantitative test called the [Shapiro-Wilk test](https://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test).\n",
    "\n",
    "To do this we use the function test_lognorm as follows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e031156",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=======================================\n",
      "Shapiro-Wilk test (lognormal):\n",
      "0.97, 0.00 (test statistic, p-value)\n",
      "It doesnt look like a lognormal distribution (p-value < 0.05)\n",
      "(╯°□°）╯︵ ┻━┻\n",
      "=======================================\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3, ax = plot.qq_plot(dataset['ECD'], figsize=(6, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b277965",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment the line below to save the figure\n",
    "#fig3.savefig('qqplot.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34eafbfa",
   "metadata": {},
   "source": [
    "## Comparing grain size distributions\n",
    "\n",
    "Let us first create a synthetic population to compare with the example population of apparent grain sizes. As an example, we will use the geometric mean and MSD of the example population to create a random lognormal population with similar characteristics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "36817222",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_dataset = averages.gen_lognorm_population(scale=np.log(30.10), # set geometric mean to 30.10\n",
    "                                              shape=np.log(1.75),  # set lognormal shape to 1.75\n",
    "                                              n=2700)              # set sample size = 2700"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1247ecb1",
   "metadata": {},
   "source": [
    "### Using the cumulative distribution function (EDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2eb2523c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e62d6eb290>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax = plt.subplots(constrained_layout=True)\n",
    "\n",
    "ax.ecdf(new_dataset, label='lognormal pop')\n",
    "ax.ecdf(dataset['ECD'], label='empirical pop')\n",
    "\n",
    "ax.set_xlabel('diameter $\\mu m$')\n",
    "ax.set_ylabel(\"Probability of occurrence\")\n",
    "ax.set_ylim(bottom=-0.03, top=1.03)\n",
    "# ax.set_xlim(right=101)\n",
    "ax.legend(loc='best', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f414f58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment the line below to save the figure\n",
    "#fig4.savefig('ecdf_comparison.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d77283f",
   "metadata": {},
   "source": [
    "### Comparing two distributions using the two-sample Kolmogorov–Smirnov test\n",
    "\n",
    "For details see GrainSizeTools documentation and the following links:  \n",
    "https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test  \n",
    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.ks_2samp.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce2645ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two-sample Kolmogorov-Smirnov test:\n",
      " KS test statistic: 0.053\n",
      "Statistic location: 33.89\n",
      "           p-value: 0.001\n"
     ]
    }
   ],
   "source": [
    "# import the two-sample Kolmogorov-Smirnov test from Scipy stats module\n",
    "from scipy.stats import ks_2samp\n",
    "\n",
    "result = ks_2samp(new_dataset, dataset['ECD'])\n",
    "\n",
    "print('Two-sample Kolmogorov-Smirnov test:')\n",
    "print(f' KS test statistic: {result.statistic:.3f}')\n",
    "print(f'Statistic location: {result.statistic_location:.2f}')\n",
    "print(f'           p-value: {result.pvalue:.3f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c7471014",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notebook last run in 2024-06-26 using:\n",
      "Python 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:16:22) [MSC v.1916 64 bit (AMD64)]\n",
      "Numpy 1.26.4\n",
      "Pandas 2.2.2\n"
     ]
    }
   ],
   "source": [
    "# annotate the date you executed the notebook and the Python version \n",
    "import sys\n",
    "from datetime import date    \n",
    "today = date.today().isoformat()\n",
    "\n",
    "print(f'Notebook last run in {today} using:')\n",
    "print('Python', sys.version)\n",
    "print('Numpy', np.__version__)\n",
    "print('Pandas', pd.__version__)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
